The present application relates generally to computers and computer applications, and more particularly to cancelable biometrics.
Biometric recognition works by obtaining a biometric signature, a physical or behavioral trait, which can be used to uniquely identify an entity from which the signature was taken. A biometric signature can be represented digitally and two biometric recognition tasks include verification and identification. Verification refers to the task of matching the same biometric signature from a single individual taken at different times, possibly, and often, with different devices. Identification refers to the task of matching the same biometric signature from a single person given a dictionary of several signatures from several individuals. Verification is referred to as a one to one (1-1) task, and identification is referred to as a one-to-many (1-N) task. The biometric matching task is usually performed by a single computing device, e.g., smart phone, laptop computer, specialized hardware.
Neural networks are computationally intelligent techniques, and can be used for accomplishing the task of biometric recognition. Neural networks can be expert classifiers that learn how to distinguish objects and aspects of objects from others based on properly labeled training data. Deep neural networks are considered well suited for analyzing large numbers (e.g., millions) of images and being able to classify specifically labeled objects or features learned from its training set on images to which it has not been previously introduced. A major feature of deep learning is layer-wise representation learning, which abstracts the features of prior layer to generate features, representative of images in reduced dimensions.